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24 from Numeric import dot
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26 from base_classes import Line_search, Min
27
28
29 -def steepest_descent(func=None, dfunc=None, args=(), x0=None, min_options=None, func_tol=1e-25, grad_tol=None, maxiter=1e6, a0=1.0, mu=0.0001, eta=0.1, full_output=0, print_flag=0, print_prefix=""):
30 """Steepest descent minimisation."""
31
32 if print_flag:
33 if print_flag >= 2:
34 print print_prefix
35 print print_prefix
36 print print_prefix + "Steepest descent minimisation"
37 print print_prefix + "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
38 min = Steepest_descent(func, dfunc, args, x0, min_options, func_tol, grad_tol, maxiter, a0, mu, eta, full_output, print_flag, print_prefix)
39 if min.init_failure:
40 print print_prefix + "Initialisation of minimisation has failed."
41 return None
42 results = min.minimise()
43 return results
44
45
47 - def __init__(self, func, dfunc, args, x0, min_options, func_tol, grad_tol, maxiter, a0, mu, eta, full_output, print_flag, print_prefix):
48 """Class for steepest descent minimisation specific functions.
49
50 Unless you know what you are doing, you should call the function 'steepest_descent' rather
51 than using this class.
52 """
53
54
55 self.func = func
56 self.dfunc = dfunc
57 self.args = args
58 self.xk = x0
59 self.func_tol = func_tol
60 self.grad_tol = grad_tol
61 self.maxiter = maxiter
62 self.full_output = full_output
63 self.print_flag = print_flag
64 self.print_prefix = print_prefix
65
66
67 self.a0 = a0
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70 self.mu = mu
71 self.eta = eta
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74 self.init_failure = 0
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77 self.line_search_options(min_options)
78 self.setup_line_search()
79
80
81 self.f_count = 0
82 self.g_count = 0
83 self.h_count = 0
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86 self.warning = None
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88
89 self.setup_conv_tests()
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91
92 self.fk, self.f_count = apply(self.func, (self.xk,)+self.args), self.f_count + 1
93 self.dfk, self.g_count = apply(self.dfunc, (self.xk,)+self.args), self.g_count + 1
94
95
97 """The new parameter function.
98
99 Find the search direction, do a line search, and get xk+1 and fk+1.
100 """
101
102
103 self.pk = -self.dfk
104
105
106 try:
107 self.a0 = self.alpha * dot(self.dfk_last, -self.dfk_last) / dot(self.dfk, -self.dfk)
108 except AttributeError:
109 "First iteration."
110 pass
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112
113 self.line_search()
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116 self.xk_new = self.xk + self.alpha * self.pk
117 self.fk_new, self.f_count = apply(self.func, (self.xk_new,)+self.args), self.f_count + 1
118 self.dfk_new, self.g_count = apply(self.dfunc, (self.xk_new,)+self.args), self.g_count + 1
119
120
122 """Function to update the function value, gradient vector, and Hessian matrix."""
123
124
125 self.fk_last = self.fk
126 self.dfk_last = self.dfk * 1.0
127
128
129 self.xk = self.xk_new * 1.0
130 self.fk = self.fk_new
131 self.dfk = self.dfk_new * 1.0
132